T-SNE result in the BCI-C IV 2a dataset.
<p>The selected subjects of the BCI-C IV 2a dataset are subject 3 and subject 7.</p>
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2024
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| _version_ | 1852025025647869952 |
|---|---|
| author | Hong-Jie Liang (11495982) |
| author2 | Ling-Long Li (20308164) Guang-Zhong Cao (6444716) |
| author2_role | author author |
| author_facet | Hong-Jie Liang (11495982) Ling-Long Li (20308164) Guang-Zhong Cao (6444716) |
| author_role | author |
| dc.creator.none.fl_str_mv | Hong-Jie Liang (11495982) Ling-Long Li (20308164) Guang-Zhong Cao (6444716) |
| dc.date.none.fl_str_mv | 2024-11-21T18:27:31Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0309706.g006 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/T-SNE_result_in_the_BCI-C_IV_2a_dataset_/27881547 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Physiology Biotechnology Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified utilizing offset parameters two public datasets mi )- electroencephalography diverse receptive fields deformable convolutional network continuous time scales computed multiple times utilizes convolution kernels dimensional convolution layer deformable convolution network convolution kernel size temporal feature extraction model &# 8217 classification accuracy obtained extracting frequency information crop classification module original eeg data frequency enhancement module dilated convolution frequency enhancement crop module temporal domain eeg data channel information baseline model utilization efficiency spatial domain important role enables motor eeg decoding disabled patients deep learning decoding plays computer interface calculating attention bci ), art methods ablation study |
| dc.title.none.fl_str_mv | T-SNE result in the BCI-C IV 2a dataset. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p>The selected subjects of the BCI-C IV 2a dataset are subject 3 and subject 7.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_a8a809fbb6dec4df7c8582a3932fffe5 |
| identifier_str_mv | 10.1371/journal.pone.0309706.g006 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/27881547 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | T-SNE result in the BCI-C IV 2a dataset.Hong-Jie Liang (11495982)Ling-Long Li (20308164)Guang-Zhong Cao (6444716)PhysiologyBiotechnologySpace ScienceBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedutilizing offset parameterstwo public datasetsmi )- electroencephalographydiverse receptive fieldsdeformable convolutional networkcontinuous time scalescomputed multiple timesutilizes convolution kernelsdimensional convolution layerdeformable convolution networkconvolution kernel sizetemporal feature extractionmodel &# 8217classification accuracy obtainedextracting frequency informationcrop classification moduleoriginal eeg datafrequency enhancement moduledilated convolutionfrequency enhancementcrop moduletemporal domaineeg datachannel informationbaseline modelutilization efficiencyspatial domainimportant roleenables motoreeg decodingdisabled patientsdeep learningdecoding playscomputer interfacecalculating attentionbci ),art methodsablation study<p>The selected subjects of the BCI-C IV 2a dataset are subject 3 and subject 7.</p>2024-11-21T18:27:31ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0309706.g006https://figshare.com/articles/figure/T-SNE_result_in_the_BCI-C_IV_2a_dataset_/27881547CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/278815472024-11-21T18:27:31Z |
| spellingShingle | T-SNE result in the BCI-C IV 2a dataset. Hong-Jie Liang (11495982) Physiology Biotechnology Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified utilizing offset parameters two public datasets mi )- electroencephalography diverse receptive fields deformable convolutional network continuous time scales computed multiple times utilizes convolution kernels dimensional convolution layer deformable convolution network convolution kernel size temporal feature extraction model &# 8217 classification accuracy obtained extracting frequency information crop classification module original eeg data frequency enhancement module dilated convolution frequency enhancement crop module temporal domain eeg data channel information baseline model utilization efficiency spatial domain important role enables motor eeg decoding disabled patients deep learning decoding plays computer interface calculating attention bci ), art methods ablation study |
| status_str | publishedVersion |
| title | T-SNE result in the BCI-C IV 2a dataset. |
| title_full | T-SNE result in the BCI-C IV 2a dataset. |
| title_fullStr | T-SNE result in the BCI-C IV 2a dataset. |
| title_full_unstemmed | T-SNE result in the BCI-C IV 2a dataset. |
| title_short | T-SNE result in the BCI-C IV 2a dataset. |
| title_sort | T-SNE result in the BCI-C IV 2a dataset. |
| topic | Physiology Biotechnology Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified utilizing offset parameters two public datasets mi )- electroencephalography diverse receptive fields deformable convolutional network continuous time scales computed multiple times utilizes convolution kernels dimensional convolution layer deformable convolution network convolution kernel size temporal feature extraction model &# 8217 classification accuracy obtained extracting frequency information crop classification module original eeg data frequency enhancement module dilated convolution frequency enhancement crop module temporal domain eeg data channel information baseline model utilization efficiency spatial domain important role enables motor eeg decoding disabled patients deep learning decoding plays computer interface calculating attention bci ), art methods ablation study |